Triple
T14709066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gremlins |
E345498
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Tina Hirsch |
E205655
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tina Hirsch | Statement: [Gremlins, editedBy, Tina Hirsch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tina Hirsch Context triple: [Gremlins, editedBy, Tina Hirsch]
-
A.
Tina Hirsch
chosen
Tina Hirsch is an American film editor known for her work on numerous feature films and television projects.
-
B.
Tina Caspary
Tina Caspary is an American actress and dancer best known for her roles in 1980s teen films and for her work as a choreographer in film and television.
-
C.
Jennifer Hirsch
Jennifer Hirsch is known as the sister of American actor Emile Hirsch.
-
D.
Denise Huth
Denise Huth is a television producer best known for her long-running work as an executive producer on AMC’s The Walking Dead franchise and its related spin-offs.
-
E.
Lisa Eilbacher
Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb9814e0c8190984ac30d276499cc |
completed | April 14, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff36455f788190a63507ecda42b04c |
completed | May 9, 2026, 1:27 p.m. |
Created at: April 10, 2026, 1:28 a.m.